APPENDIX B pspectrum.m

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APPENDIX B

pspectrum.m

% % FUNCTION pspectrum %

% Display the sound power spectrum (in linear and logarithmic scales) % function r = pspectrum(s, fs) l = length(s); m = 100; n = 256; nbFrame = floor((l - n) / m ) + 1; %

% Create a matrix M containg all the frames % for i = 1:n for j = 1:nbFrame M(i, j) = s(((j - 1) * m) + i); end end %

% Matrix M created. Now apply HAMMING window and store in matrix N. Column vectors of N are

% the original frame vectors transformed by the Hamming window filter %

h = hamming(n); N = diag(h) * M;

%

% Now apply FFT and create a new matrix M2 where the column vectors are the FFTs of the

% column vectors of N. The elements of column matrix M2 contain the frames of the original

% signal, filtered by the Hamming window and transformed with the FFT. The elements of M2

% are complex numbers and symmetrical because FFT was used to transform the data.

%

% Each column in M2 is a power spectrum of the original signal %

for i = 1:nbFrame

M2(:,i) = fft(N(:, i)); end

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tm = length(s) / fs; subplot(121);

imagesc([0 tm], [0 fs/2], abs(M2(1:t, :)).^2), axis xy; title('Power spectrum (Linear)');

xlabel('Time [s]');

ylabel('Frequency [Hz]'); colorbar;

subplot(122);

imagesc([0 tm], [0 fs/2], 20*log10(abs(M2(1:t, :)).^2)), axis xy; title('Power spectrum (Logarithmic)');

xlabel('Time [s]');

ylabel('Frequency [Hz]'); colorbar;

r=1;

%

% END OF FUNCTION pspectrum %